{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ohlcv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import vectorbt as vbt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from datetime import datetime, timedelta\n",
    "from numba import njit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Config({\n",
      "    \"open\": \"Open\",\n",
      "    \"high\": \"High\",\n",
      "    \"low\": \"Low\",\n",
      "    \"close\": \"Close\",\n",
      "    \"volume\": \"Volume\"\n",
      "})\n"
     ]
    }
   ],
   "source": [
    "print(vbt.settings.ohlcv['column_names'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Can only use .str accessor with string values!\n"
     ]
    }
   ],
   "source": [
    "try:\n",
    "    pd.DataFrame([1, 2, 3]).vbt.ohlcv.plot()\n",
    "except Exception as e:\n",
    "    print(e) # couldn't find default column names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "ohlcv_ts = pd.DataFrame({\n",
    "    'open': [1, 2, 3], \n",
    "    'high': [1, 2, 3], \n",
    "    'low': [1, 2, 3], \n",
    "    'close': [1, 2, 3], \n",
    "    'volume': [1, 2, 3]\n",
    "})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                open      high       low     close    volume\n",
      "2018-01-01 -0.385236 -0.339812 -0.941729 -1.058755 -0.881574\n",
      "2018-01-02 -0.243403  0.210847 -0.408890  0.158420 -1.149656\n",
      "2018-01-03  0.226744  1.207481  0.198341  1.168544 -2.071525\n",
      "2018-01-04  0.407461  1.294365 -0.223333  0.946101 -2.218826\n",
      "2018-01-05  1.221150  1.988841  0.810106  0.834179 -2.940705\n",
      "2018-01-06  2.022596  2.046477  1.293526  2.013281 -2.689034\n",
      "2018-01-07  2.724580  3.303932  1.976807  3.468771 -1.720546\n",
      "2018-01-08  2.057622  2.410773  1.251777  2.269435 -0.980795\n",
      "2018-01-09  1.607800  1.720066  1.169075  2.456895 -1.621470\n",
      "2018-01-10  2.069398  2.516643  1.838688  1.776437 -1.282516\n"
     ]
    }
   ],
   "source": [
    "ohlcv_ts = pd.DataFrame(\n",
    "    columns=['open', 'high', 'low', 'close', 'volume'],\n",
    "    index=[datetime(2018, 1, 1) + timedelta(days=i) for i in range(10)]\n",
    ")\n",
    "ohlcv_ts['open'] = np.cumsum(np.random.uniform(-0.8, 1, size=(10,)))\n",
    "ohlcv_ts['close'] = ohlcv_ts['open'] + np.random.uniform(-1, 1, size=(10,))\n",
    "ohlcv_ts['high'] = ohlcv_ts['open'] + np.random.uniform(0, 1, size=(10,))\n",
    "ohlcv_ts['low'] = ohlcv_ts['open'] - np.random.uniform(0, 1, size=(10,))\n",
    "ohlcv_ts['volume'] = np.cumsum(np.random.uniform(-1, 1, size=(10,)))\n",
    "\n",
    "print(ohlcv_ts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "try:\n",
    "    ohlcv_ts.vbt.ohlcv.plot()\n",
    "except Exception as e:\n",
    "    print(e) # still couldn't find default column names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
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    }
   ],
   "source": [
    "# Specify them manually\n",
    "ohlcv_ts.vbt.ohlcv(column_names={\n",
    "    'open': 'open', \n",
    "    'high': 'high', \n",
    "    'low': 'low', \n",
    "    'close': 'close', \n",
    "    'volume': 'volume'\n",
    "}).plot().show_svg()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Or by changing the defaults\n",
    "vbt.settings.ohlcv['column_names'] = {\n",
    "    'open': 'open', \n",
    "    'high': 'high', \n",
    "    'low': 'low', \n",
    "    'close': 'close', \n",
    "    'volume': 'volume'\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ohlcv_ts.vbt.ohlcv.plot().show_svg()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Start             2018-01-01 00:00:00\n",
       "End               2018-01-10 00:00:00\n",
       "Period               10 days 00:00:00\n",
       "First Price                 -0.385236\n",
       "Lowest Price                -1.058755\n",
       "Highest Price                3.468771\n",
       "Last Price                   1.776437\n",
       "First Volume                -0.881574\n",
       "Lowest Volume               -2.940705\n",
       "Highest Volume              -0.881574\n",
       "Last Volume                 -1.282516\n",
       "Name: agg_func_mean, dtype: object"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ohlcv_ts.vbt.ohlcv.stats()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7.62 ms ± 11.2 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
     ]
    }
   ],
   "source": [
    "big_ohlcv_ts = pd.DataFrame(np.random.uniform(1, 2, (10000, 5)), columns=ohlcv_ts.columns)\n",
    "\n",
    "%timeit big_ohlcv_ts.vbt.ohlcv.stats()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
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